SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
A microstructure scale electrochemical LIB model was used to investigate lithium plating onset, material non-uniform utilization, and in-plane heterogeneities for an NMC-graphite full cell. Model predicts active material particle surface roughness and size distribution (respectively, non-uniform curvature within and between particles) initiate in-plane heterogeneity, and that particle size heterogeneity at the separator interface controls the lithium plating preferential deposition ("Where"). These in-plane heterogeneities are then exacerbated by through-plane heterogeneities induced at fast charge as electrolyte depletion occurs and concentrates intercalation reaction near the anode-separator interface. Also, magnitude and occurrence of lithium plating is controlled by effective, or macroscale, microstructure parameters ("When"). As local states of charge start to diverge between nearby active material regions, overpotential differences induced by OCP difference kick in and contribute to reduce these SOC local heterogeneities. However, for staged materials such as graphite, with OCP profile alternating between plateaus and varying regions, this balancing mechanism is, respectively, inactive and active. This leads to a dynamic, non-monotonic, in-plane heterogeneity time evolution for state of charge and Faraday current density, for which their respective in-plane heterogeneity magnitude alternates. Such behavior has been modeled both for the whole electrode at the microstructure scale and at the particle scale. In-plane heterogeneities are usually considered to be detrimental, as they result in material non-uniform utilization (i.e., under and over stressed regions) and earlier degradations. However, this work provides a more granular approach as it discriminates between a harmful in-plane heterogeneity (non-uniform curvature) that triggers SOC in-plane heterogeneity, and a beneficial in-plane heterogeneity (Faraday current density) that contributes to reduce SOC in-plane heterogeneity. This work comprehensively explains the mechanisms that initiate, exacerbate, and regulate heterogeneity at the microstructure scale, while providing some design suggestions to reduce both in-plane and through-plane heterogeneities, as summarized in the graphical abstract.
Single atom, low valent transition metals are important for heterogeneous catalysis but are challenging to generate and stabilize in a well-defined manner. Herein, we explored the functionalization of silica with well-defined N-heterocyclic phosphenium (NHP) ions to heterogenize low-valent metals. The surface electro-statically bound [NHP] + coordinate to Pt(0) precursors resulting in well-defined, chemisorbed [(NHP)Pt(0)L n ] + sites. The resulting materials catalyze the hydrosilylation of alkynes and exhibit activities and selectivities that rival the current industry standard homogeneous catalysts. The catalysts leach Pt limiting their recyclability; however, recycling studies support that the high regioselectivities arise from heterogeneous sites and Pt particles do not form on the surface. Here we suspect that this phosphenium-based immobilization strategy will result in stable, tunable, low valent heterogeneous transition metal catalysts in a wider array of catalytic reactions.
Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.
Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent β in a model tetrahedral liquid ZnCl 2 from T m + 85 K to T m + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl 2 . We found a non-monotonic temperature dependence of β from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. Furthermore, this result may contribute to new understandings of the structural relaxation of other network liquids.
Capillary heterogeneity has been identified over the last decade as a key control on subsurface CO 2 flow behavior during geological CO 2 sequestration. These heterogeneities can be formed in all sedimentary rocks, ranging from slight variations in the sand grain sizes to extensive sequences of interbedded sands, shales, and limestones. Capillary heterogeneity has been largely, although not entirely, overlooked in subsurface flow modeling because it is assumed to only directly influence fluid redistribution over scales of centimeters to meters. However, even small-scale fluid movements can result in dramatic impacts on the mobility and trapping of the CO 2 over kilometers. Therefore, neglecting capillary heterogeneity at multiple scales could potentially lead to errors in modeling and predicting field-scale plume migration. In this review paper, we aim to provide a consistent overview to (1) establish that capillary heterogeneity can have a major impact on CO 2 plume migration, (2) establish the respective length scales at which capillary heterogeneity matters, and (3) provide guidance for numerical modeling. This review covers pertinent literature and extracts key observations from the core to the field scales. Experimental studies have shown that millimeter-decimeter scale capillary heterogeneity can cause the so-called capillary heterogeneity trapping in addition to pore-scale residual trapping. Even at such a small scale, capillary heterogeneity can already lead to complex upscaled constitutive relationships, such as flow-rate dependent and anisotropic relative permeability, which affects field-scale CO 2 migration even when field-scale heterogeneities are present. Under gravity-dominated flow regimes, centimeter-meter scale capillary heterogeneity can entrap a significant amount of CO 2 at field scale, not just after imbibition but also during drainage. In certain cases, the presence of capillary heterogeneity can even completely stop the vertical movement of the CO 2 plume, hence greatly reducing leakage risks. At meter-kilometer scale, the influence of capillary heterogeneity is more pronounced and can hinder or redirect CO 2 migration in both lateral and vertical directions. The impact of capillary heterogeneity across multiple spatial scales poses a great challenge in modeling CO 2 migration at field scale, because it is practically impossible to build a field-scale earth model with grid blocks at millimeter scale. We recommend a hierarchical modeling approach to address this challenge. At field scale, earth models are built to capture geological features and heterogeneities in high but still practical grid resolutions. For each facies or rock type of the field-scale model, high- resolution meter-scale “conceptual” models are built with millimeter-scale grid blocks to capture representative fine-scale bedding geometries and heterogeneities in various environments of deposition, bridging the gap from subcore scale to the size of a field-scale simulation grid block. Upscaling is then used to preserve the smaller-scale flow dynamics of various rock types in field-scale simulations. Here, future work is needed to (1) refine, improve, and validate the hierarchical modeling approach; (2) build libraries of fine-scale bedding models for facies in various environments of deposition; (3) quantify multiscale capillary heterogeneity effects under subsurface uncertainties; (4) gain learning from different storage formations; and (5) establish best practices that balance accuracy and computational speed.
Recent theoretical investigations on graphite particle combustion have employed several levels of heterogeneous reaction models, ranging from global to elementary models, to describe the oxidation of carbon to gaseous products. Unlike the counterpart homogeneous reaction models, these heterogeneous reaction models are not well developed because of the difficulties associated with decoupling the physical characteristics of the solid (e.g. surface area taking part in combustion) from the chemical kinetic data. This is certainly true for porous graphite particle combustion, where heterogeneous and homogeneous reactions occur within the pores and play an important role in the overall oxidation process. As a result, there are considerable uncertainties of physical phenomena predicted using different heterogeneous kinetic models available in the literature. A good example, discussed later in this paper, is the predicted critical particle size below which the mass burning rate becomes exponentially small. The main goal of this study is to understand the basic mechanism controlling such rapid changes in burning rates, by developing a model where physical contributions are decoupled from chemical rate constants in a consistent manner. Another important goal of the proposed study is to develop a truly intrinsic, detailed heterogeneous reaction model for porous graphite combustion at high-temperatures, and to derive a systematically reduced heterogeneous reaction model in terms of the elementary reaction rate constants of the detailed model. The validation of chemical kinetic models describing the heterogeneous and homogeneous combustion in and around a spherically symmetric porous graphite particle can be considerably simplified by experimental measurements obtained under microgravity conditions. A vital component of this study is to conduct such supporting experiments on particle burning rate and surface temperature using NASA microgravity facilities, in close coordination with the theoretical effort. The basic understanding obtained and models developed as part of this project will be useful for optimal design of coal combustion devices. These models can also be extended to investigate the role of heterogeneous chemistry on pollutant formation pathways in combustion devices. The theoretical approach developed here, with pore diffusion effects decoupled from the chemical effects, can also be extended to understand the heterogeneous combustion of other porous fuels, for example, combustion of magnesium in a CO2 environment for propulsion in the Martian atmosphere.
Noisy Intermediate-Scale Quantum Computing (NISQ) has dominated headlines in recent years, with the longer-term vision of Fault-Tolerant Quantum Computation (FTQC) offering significant potential but at currently intractable resource costs and quantum error correction (QEC) overheads. For problems of interest, FTQC will require millions of physical qubits with long coherence times, high-fidelity gates, and compact sizes to surpass classical systems. Just as heterogeneous specialization has offered scaling benefits in classical computing, it is likewise gaining interest in FTQC. However, systematic use of heterogeneity in either hardware or software elements of FTQC systems remains a serious challenge due to the vast design space and the variable physical constraints. This paper meets the challenge of making heterogeneous FTQC design practical by introducing HetArch, a toolbox for designing heterogeneous quantum systems, and using it to explore heterogeneous design scenarios. Using a hierarchical approach, we successively break quantum algorithms into smaller operations (akin to classical application kernels), thus greatly simplifying the design space and resulting tradeoffs. Specializing to superconducting systems, we then design optimized heterogeneous hardware composed of varied superconducting devices, abstracting physical constraints into design rules that enable devices to be assembled into standard cells optimized for specific operations, which, in turn, form heterogeneous modules optimized for quantum subroutines. Finally, we provide a heterogeneous design space exploration framework which reduces the simulation burden by a factor of 10^4 or more and allows us to characterize optimal design points. We use these techniques to design superconducting quantum modules for entanglement distillation, error correction, and code teleportation, reducing error rates by 2.6×, 10.7×, and 3.4× compared to homogeneous systems.
We examine the influence of surface heterogeneity on boundary layers using a large-eddy simulation coupled to a land-surface model. Heterogeneity, imposed in strips varying from 2-30 km (1 less than lambda/z(sub i) less than 18), is found to dramatically alter the structure of the free convective boundary layer by inducing significant organized circulations. A conditional sampling technique, based on the scale of the surface heterogeneity (phase averaging), is used to identify and quantify the organized surface fluxes and motions in the atmospheric boundary layer. The impact of the organized motions on turbulent transport depends critically on the scale of the heterogeneity lambda, the boundary layer height zi and the initial moisture state of the boundary layer. Dynamical and scalar fields respond differently as the scale of the heterogeneity varies. Surface heterogeneity of scale 4 less than lamba/z(sub i) less than 9 induces the strongest organized flow fields (up, wp) while heterogeneity with smaller or larger lambda/z(sub i) induces little organized motion. However, the organized components of the scalar fields (virtual potential temperature and mixing ratio) grow continuously in magnitude and horizontal scale, as lambda/z(sub i) increases. For some cases, the organized motions can contribute nearly 100% of the total vertical moisture flux. Patch-induced fluxes are shown to dramatically impact point measurements that assume the time-average vertical velocity to be zero. The magnitude and sign of this impact depends on the location of the measurement within the region of heterogeneity.
Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian particle dispersion model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined. In addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near- Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the formation of mesoscale circulation patterns that are primarily responsible for non-Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land-surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skillful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes.
It has long been conjectured that thermal heterogeneity at the core-mantle boundary (CMB) affects the geodynamo substantially. The observed two pairs of steady and strong magnetic flux lobes near the Polar Regions and the low secular variation in the Pacific over the past 400 years (and perhaps longer) are likely the consequences of this CMB thermal heterogeneity. There are several studies on the impact of the thermal heterogeneity with numerical geodynamo simulations. However, direct correlation between the numerical results and the observations is found very difficult, except qualitative comparisons of certain features in the radial component of the magnetic field at the CMB. This makes it difficult to assess accurately the impact of thermal heterogeneity on the geodynamo and the geomagnetic secular variation. We revisit this problem with our MoSST_DAS system in which geomagnetic data are assimilated with our geodynamo model to predict geomagnetic secular variations. In this study, we implement a heterogeneous heat flux across the CMB that is chosen based on the seismic tomography of the lowermost mantle. The amplitude of the heat flux (relative to the mean heat flux across the CMB) varies in the simulation. With these assimilation studies, we will examine the influences of the heterogeneity on the forecast accuracies, e.g. the accuracies as functions of the heterogeneity amplitude. With these, we could be able to assess the model errors to the true core state, and thus the thermal heterogeneity in geodynamo modeling.
Small-scale heterogeneities can significantly affect the fate of the CO 2 plume and trapping during CO 2 migration. We conducted geologic carbon storage field-scale simulations to investigate the impact of small-scale heterogeneities on plume dynamics and trapping performance. Small-scale heterogeneities have been shown to increase the amount of trapped CO 2 during buoyancy-driven flow. The trapped CO 2 saturation is validated by previous sandbox experimental work during buoyancy-driven flow in realistic heterogeneous domains and is implemented through the critical CO 2 saturation parameter (i.e., the first non-zero value in the drainage CO 2 relative permeability curve). Depending on the type and degree of heterogeneity, various critical CO 2 saturation values are exhibited. Furthermore, we investigated the effect of small-scale heterogeneities when multiple capillary pressure models are employed. This study demonstrates that an increase in critical CO 2 saturation reduces the CO 2 plume size and lateral extent, accompanying an increase in residual trapping and a decrease in solubility trapping. Lastly, we show that independent of the capillary pressure model used, an increase in critical saturation leads to similar CO 2 plume dynamics distribution and trapping performance. These results emphasize the importance of quantifying the effect of small-scale heterogeneity as they affect the large-scale behavior of the CO 2 plume.
Our environment is heterogeneous. In hydrological sciences, the heterogeneity of subsurface properties, such as hydraulic conductivities or porosities, exerts an important control on water balance. This notably includes groundwater recharge, which is an important variable for efficient and sustainable groundwater resources management. Current large-scale hydrological models do not adequately consider this subsurface heterogeneity. Here we show that regions with strong subsurface heterogeneity have enhanced present and future recharge rates due to a different sensitivity of recharge to climate variability compared with regions with homogeneous subsurface properties. Our study domain comprises the carbonate rock regions of Europe, Northern Africa, and the Middle East, which cover 25 of the total land area. We compare the simulations of two large-scale hydrological models, one of them accounting for subsurface heterogeneity. Carbonate rock regions strongly exhibit karstification, which is known to produce particularly strong subsurface heterogeneity. Aquifers from these regions contribute up to half of the drinking water supply for some European countries. Our results suggest that water management for these regions cannot rely on most of the presently available projections of groundwater recharge because spatially variable storages and spatial concentration of recharge result in actual recharge rates that are up to four times larger for present conditions and changes up to five times larger for potential future conditions than previously estimated. These differences in recharge rates for strongly heterogeneous regions suggest a need for groundwater management strategies that are adapted to the fast transit of water from the surface to the aquifers.
A novel geometry approximation scheme named ring-heterogeneous approximation for intermediate resolution multiphysics analysis of SFRs is presented. This approach particularly targets neutronics coupled with thermo-mechanics feedback, which dominate the passive safety of SFR designs. The ring-heterogeneous approximation casts an array of pins into volume-preserving bands of materials that constitute the pins. It allows to efficiently simulate SFRs with an unstructured mesh and also to explicitly consider the deformation of each material by retaining their heterogeneity. Verification results with single assembly and core problems of the ABTR benchmark demonstrated that the ring-heterogeneous approximation is a reasonable approximation for SFRs. For a 2D full core problem, the ring-heterogeneous approximation significantly reduced the number of elements by at least a factor of 70 from a fully-heterogeneous mesh while almost exactly preserving the reference fully-heterogeneous solutions with the RMS error of only 0.02% in the ring-wise fission reaction rate.
Task-based programming models and execution paradigms provide a means to decompose a computation by expressing it as a graph in which each node represents a specific computation operating on memory objects and the edges define the dependencies in the execution flow. In this execution model, independent nodes in the graph can be executed concurrently in different computing devices, making it suitable for heterogeneous systems in which computing devices with different architectures coexist. However, careful memory orchestration across heterogeneous devices is needed because copies of the same memory object may reside in multiple devices during execution. Manually ensuring such an orchestration is quite challenging. Not only must an application developer guard against race conditions, but they must also optimize data movement between the host and devices because unnecessary data movement significantly impacts performance. To mitigate these challenges, we enhance the IRIS heterogeneous runtime and introduce IRIS-MEMFLOW–a data flow–enabled portable memory abstraction for seamlessly orchestrating memory in diverse heterogeneous computing environments. By using data-flow analysis, IRIS-MEMFLOW guards against race conditions while multiple heterogeneous devices access memory objects. IRIS-MEMFLOW also optimizes data movement between the host and devices without manual intervention. As a result, IRIS provides improved programming productivity, performance, and portability for multidevice heterogeneous executions in high-performance computing and cloud systems that run diverse architectures from different vendors. The efficacy of IRIS-MEMFLOW is evaluated through experiments that show its capability in terms of programming productivity, multidevice heterogeneity, portability, and low overhead versus the state of the art.
In the rapidly evolving field of high-performance computing (HPC), effectively leveraging heterogeneous devices through asynchronous task programming is paramount. This paper presents a robust asynchronous task programming model tailored for a multi-device, multi-stream execution environment that incorporates a diverse array of heterogeneous computing units, including GPUs from various vendors and other accelerators. Current state-of-the-art task programming models provide methodologies to support asynchronous task executions, but they typically handle homogeneous devices using native programming languages, while support for heterogeneous devices is limited to frameworks like OpenCL. This gap presents significant challenges in abstracting heterogeneous devices to harness their true asynchronous capabilities effectively using their native programming languages. By implementing asynchronous task execution, our model significantly boosts the performance of tiled algorithm task graphs through overlapping data transfers with computation and enabling the simultaneous execution of multiple kernels. We integrate this approach into a heterogeneous Intelligent Runtime System (IRIS) and assess its performance using a suite of tiled algorithm benchmarks from the heterogeneous math kernels library (MatRIS) based on IRIS. Experimental results demonstrate a performance improvement ranging from 1.6 × to 2 × over IRIS without asynchronous support, and a notable 22% performance enhancement compared to established runtime systems such as StarPU and PaRSEC. This approach significantly improves computation efficiency of HPC workflows and provides a solid base for future exploration and development in the area of asynchronous task programming in heterogeneous systems.
Distinguishing whether a seismic event is an earthquake or an explosion is a core problem in explosion monitoring. For simplistic models, earthquakes produce a predictable S-wavefield based on their radiation pattern, while an explosion produces no S-wavefield. However, observations from nuclear tests show that explosions can produce significant S-wave energy on both horizontal components. We perform numerical experiments using SPECFEM to constrain when an S-wavefield generated from an explosion might differ from one produced by an earthquake. We generate the S-wavefield for our explosions by placing the source location within a small region consisting of large velocity heterogeneities. For the 2D cases investigated here, a crucial condition is that the size of the region of heterogeneities must be much smaller (approximately 1/12th) than the minimum wavelength of the wavefield for our simulation setups. We investigate both isotropic heterogeneities and anisotropic heterogeneities. Initial results demonstrate that the heterogeneous region lowers the P/S amplitude ratios of an explosion. The presence of strong anisotropic heterogeneities near the source can also produce an S-wavefield with similar amplitudes to the P-wavefield. Lastly, we show that S-wave polarization angles vary as a function of azimuth and distance for explosive sources in the presence of small-scale heterogeneities.
Most engineering materials, including some metals, most notably aluminum, burn in homogeneous combustion. 'Homogeneous' refers to both the fuel and the oxidizer being in the same phase, which is usually gaseous. The fuel and oxidizer are well mixed in the combustion reaction zone, and heat is released according to some relation like q(sub c) = delta H(sub c)c[((rho/rho(sub 0))]exp a)(exp -E(sub c)/RT), Eq. (1) where the pressure exponent a is usually close to unity. As long as there is enough heat released, combustion is sustained. It is useful to conceive of a threshold pressure beyond which there is sufficient heat to keep the temperature high enough to sustain combustion, and beneath which the heat is so low that temperature drains away and the combustion is extinguished. Some materials burn in heterogeneous combustion, in which the fuel and oxidizer are in different phases. These include iron and nickel based alloys, which burn in the liquid phase with gaseous oxygen. Heterogeneous combustion takes place on the surface of the material (fuel). Products of combustion may appear as a solid slag (oxide) which progressively covers the fuel. Propagation of the combustion melts and exposes fresh fuel. Heterogeneous combustion heat release also follows the general form of Eq.(1), except that the pressure exponent a tends to be much less than 1. Therefore, the increase in heat release with increasing pressure is not as dramatic as it is in homogeneous combustion. Although the concept of a threshold pressure still holds in heterogeneous combustion, the threshold is more difficult to identify experimentally, and pressure itself becomes less important relative to the heat transfer paths extant in any specific application. However, the constants C, a, and E(sub c) may still be identified by suitable data reduction from heterogeneous combustion experiments, and may be applied in a heat transfer model to judge the flammability of a material in any particular actual-use situation. In order to support the above assertions, two investigations are undertaken: 1) PCT data are examined in detail to discover the pressure dependence of heterogeneous combustion experiment results; and 2) heterogeneous combustion in a PCT situation is described by a heat transfer model, which is solved first in simplified form for a simple actual-use situation, and then extended to apply to PCT data reduction (combustion constant identification).